class_docstr: Leave One Feature Out Importance
  Given a model and cross-validation scheme, calculates the feature importances.
  
  Parameters
  ----------
  dataset: LOFO Dataset object
  scoring: string or callable
      Same as scoring in sklearn API
  model: model (sklearn API), optional
      Not trained model object
  fit_params : dict, optional
      fit parameters for the model
  cv: int or iterable
      Same as cv in sklearn API
  n_jobs: int, optional
      Number of jobs for parallel computation
  cv_groups: array-like, with shape (n_samples,), optional
      Group labels for the samples used while splitting the dataset into train/test set. 
      Only used in conjunction with a “Group” cv instance (e.g., GroupKFold).
code: def _get_cv_score(self, feature_to_remove):
      X, fit_params = self.dataset.getX(
          feature_to_remove=feature_to_remove,
          fit_params=self.fit_params,
      )
      y = self.dataset.y
  
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          cv_results = cross_validate(
              self.model,
              X,
              y,
              cv=self.cv,
              scoring=self.scoring,
              fit_params=fit_params,
              groups=self.cv_groups,
          )
      return cv_results["test_score"]
  
file_path: lofo-importance/lofo/lofo_importance.py
imports: import numpy
  from sklearn.model_selection import cross_validate
  from tqdm.autonotebook import tqdm
  import warnings
  from lofo.infer_defaults import infer_model
  from lofo.utils import lofo_to_df, parallel_apply
label_desc: Use image node_id 2 for calling the LOFOImportance obj's underlying member method code with example usage: obj._get_cv_score(feature_to_remove) and returns: cv_results